Why are Text Search abstractions needed?
When dealing with text prompts or text content in chat history a common requirement is to provide additional relevant information related to this text. This provides the AI model with relevant context which helps it to provide more accurate responses. To meet this requirement the Semantic Kernel provides a Text Search abstraction which allows using text inputs from various sources, e.g. Web search engines, vector stores, etc., and provide results in a few standardized formats.
Note
Search for image content or audio content is not currently supported.
Text search abstraction
The Semantic Kernel text search abstractions provides three methods:
SearchGetSearchResultsGetTextSearchResults
Search
Performs a search for content related to the specified query and returns string values representing the search results. Search can be used in the most basic use cases e.g., when augmenting a semantic-kernel format prompt template with search results. Search always returns just a single string value per search result so is not suitable if citations are required.
GetSearchResults
Performs a search for content related to the specified query and returns search results in the format defined by the implementation. GetSearchResults returns the full search result as defined by the underlying search service. This provides the most versatility at the cost of tying your code to a specific search service implementation.
GetTextSearchResults
Performs a search for content related to the specified query and returns a normalized data model representing the search results. This normalized data model includes a string value and optionally a name and link. GetTextSearchResults allows your code to be isolated from the a specific search service implementation, so the same prompt can be used with multiple different search services.
Tip
To run the samples shown on this page go to GettingStartedWithTextSearch/Step1_Web_Search.cs.
The sample code below shows each of the text search methods in action.
using Microsoft.SemanticKernel.Data;
using Microsoft.SemanticKernel.Plugins.Web.Bing;
// Create an ITextSearch instance using Bing search
var textSearch = new BingTextSearch(apiKey: "<Your Bing API Key>");
var query = "What is the Semantic Kernel?";
// Search and return results
KernelSearchResults<string> searchResults = await textSearch.SearchAsync(query, new() { Top = 4 });
await foreach (string result in searchResults.Results)
{
Console.WriteLine(result);
}
// Search and return results as BingWebPage items
KernelSearchResults<object> webPages = await textSearch.GetSearchResultsAsync(query, new() { Top = 4 });
await foreach (BingWebPage webPage in webPages.Results)
{
Console.WriteLine($"Name: {webPage.Name}");
Console.WriteLine($"Snippet: {webPage.Snippet}");
Console.WriteLine($"Url: {webPage.Url}");
Console.WriteLine($"DisplayUrl: {webPage.DisplayUrl}");
Console.WriteLine($"DateLastCrawled: {webPage.DateLastCrawled}");
}
// Search and return results as TextSearchResult items
KernelSearchResults<TextSearchResult> textResults = await textSearch.GetTextSearchResultsAsync(query, new() { Top = 4 });
await foreach (TextSearchResult result in textResults.Results)
{
Console.WriteLine($"Name: {result.Name}");
Console.WriteLine($"Value: {result.Value}");
Console.WriteLine($"Link: {result.Link}");
}
Coming soon
More coming soon.
Coming soon
More coming soon.